State estimator

1. An apparatus for a motor vehicle driver assistance system for an ego vehicle, the apparatus comprising:

one or more sensors; and

an electronic control unit configured to implement a state estimator configured to use a first state of the ego vehicle to calculate a second state of the ego vehicle by using:

a prediction model to estimate the second state from the first state; and

an update model to refine the estimated second state on the basis of at least one value corresponding to a measurement of the second state, the at least one value being determined from at least one sensor measurement from the one or more sensors;

wherein to calculate the second state from the first state the electronic control unit is configured to use an artificial neural network (“ANN”); and

wherein the update model is an update ANN, and wherein an input vector to the update ANN comprises at least a portion of the estimated second state and the at least one value corresponding to the measurement of the second state, and an output vector of the update ANN is at least a portion of the second state, and wherein the update ANN is configured to correct one or more inaccuracies of the estimated second state produced by the prediction model.

2. An apparatus according to claim 1 , wherein the prediction model is a prediction ANN.

3. An apparatus according to claim 2 , wherein an input vector to the prediction ANN comprises at least a portion of the first state, and an output vector of the prediction model ANN is at least a portion of the estimated second state.

4. An apparatus according to claim 1 , wherein the update model and the prediction model are combined into a combined ANN, an input vector to the combined ANN comprises at least a portion of the first state and the at least one value determined from the at least one sensor measurement, and an output vector of the combined ANN forms at least a portion of the second state.

5. An apparatus according to claim 1 , wherein the first state and the second state each include at least one ego vehicle attribute describing an aspect of a motion of the ego vehicle.

6. An apparatus according to claim 1 , wherein the first state and the second state each include at least one local object attribute describing a local object located in the vicinity of the ego vehicle.

7. An apparatus according to claim 6 , wherein the at least one local object attribute includes a location of the local object.

8. An apparatus according to claim 7 , wherein the ANN is configured to estimate a second location of the local object in the estimated second state using a first location of the local object in the first state.

9. An apparatus according to claim 6 , wherein the local object is a local vehicle.

10. An apparatus according to claim 9 , wherein the at least one value corresponding to the measurement of the second state includes a measurement of the second location of the local vehicle.

11. An apparatus according to claim 1 , wherein the apparatus is configured to output an output variable from the second state for use by an active driver assistance device or a passive driver assistance device.

12. An apparatus according to claim 1 , wherein the apparatus is configured to output an output variable from the second state for presentation to a driver of the ego vehicle.

13. An apparatus according to claim 1 , wherein the first state and the second state each include at least one environment attribute describing an environment in which the ego vehicle is located.

14. An apparatus according to claim 1 , wherein the update ANN includes a first neuron, in a first layer of the ANN, configured to use as an input at least one output of a second neuron, the first layer of the ANN, as the first neuron or a second layer of the ANN that is subsequent to the first layer of the ANN.

15. A method for estimating a state of an ego vehicle, the state being for use in a motor vehicle driver assistance system for the ego vehicle, the method comprising:

making at least one sensor measurement using one or more sensors of the ego vehicle; and

calculating a second state of the ego vehicle from a first a first state of the ego vehicle using a state estimator by:

using a prediction model to estimate the second state from the first state; and

using an update model to refine the estimated second state on the basis of at least one value corresponding to a measurement of the second state, the at least one value being determined from the at least one sensor measurement;

wherein calculating the second state from the first state includes using an artificial neural network (“ANN”); and

wherein the update model is an update ANN, and wherein an input vector to the update ANN is formed from at least a portion of the estimated second state and the at least one value corresponding to the measurement of the second state, and an output vector of the update ANN is at least a portion of the second state, and wherein the update ANN is configured to correct one or more inaccuracies of the estimated second state produced by the prediction model.